The long-term goal of the Program Project is to attack the increasingly prevalent problem of protease-inhibitor-induced drug resistance in HIV infection. Project #1 will develop a hierarchical computational model of HIV-1 drug therapy to be used to design and evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes a structural representation of drug-protease interaction at atomic detail, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modeling of drug resistance mutations in the face of selection pressures by drug, and a mathematical description of viral population dynamics in infected individuals. Computational biology and informatics approaches will be utilized, coupled with cell culture and patient data on HIV-1 protease resistance evolution. Structural analyses and functional assays will be used to generate and refine these models. These techniques will be used for design of resistance-evading inhibitors by computational coevolution and for the optimization of existing protease inhibitors to improve their robust ness in the race of resistance mutation. The goals point the way to a new synthesis of structure-based design, informatics, and experimental approaches within the larger biological and therapeutic context. Specific Aims: 1. Develop and apply computational coevolution at atomic detail to generate models of drug resistance and to aid in the design of robust inhibitors of HIV-Pr and variants. 2. Develop and apply models of HIV population dynamics under protease inhibitor selection pressure, utilizing both experimental cell culture and patient data as well as results from computations from Specific Aim 1. 3. Develop and apply automated learning methods, including Bayesian networks and genetic algorithms, to refine models of immune response, HIV quasi-species population dynamics and drug/virus resistance coevolution.